Self-improving, automated, intelligent product finder and guide

Abstract

A self-improving, automated, highly intelligent product finder/guide can be used by a user to access a web site running the computer recommendation engine to look for product in segment X (e.g., pool floats). The user takes a quiz, and the quiz recommends a product. The user is then taken to the seller's site to buy product. If user buys, the seller may pay a commission for having the buyer directed to the seller's site. The user can give feedback and suggestions, including suggesting new questions. Some goals of the system include short-term goals to increase the probability that user buys a product, and long-term goals to have the system of the present invention be the trusted source for people to find out which product is best for them. Reinforcement learning (RL) methods can be used to optimize these measurable quantities.

What is claimed is: 
     
         1 . A method for providing a product recommendation to a user via a computer system, the method comprising:
 automatically generating, by the computer system, an interaction tree/graph comprising questions, answers and responses for a particular set of products;   receiving input from the user as user answers to the automatically generated questions; and   recommending a product from the particular set of products to the user.   
     
     
         2 . The method of  claim 1 , wherein the interaction tree/graph is generated by a machine learning technique including decision tree algorithms. 
     
     
         3 . The method of  claim 1 , further comprising building the interaction graph/tree by initially associating question answers with sets of compatible products and limiting the sets of compatible products as a user answers the questions. 
     
     
         4 . The method of  claim 1 , further comprising automatically generating the questions, answers and responses based on a database of product-question-response relationships. 
     
     
         5 . The method of  claim 1 , further comprising automatically generating the questions, answers, responses and/or recommendations based on at least one of data harvested by the computer system, crowd-sourced suggestions, and personality based questions, answers, responses and recommendations. 
     
     
         6 . The method of  claim 1 , further comprising automatically generating the questions, answers, responses and/or recommendations based on at least one of data that includes manufacturer data, data from sellers of one or more of the particular set of products, and/or data from consumer reviews, where the data is obtained via web scraping and optionally extracted using natural language processing. 
     
     
         7 . The method of  claim 1 , further comprising optimizing the interaction tree/graph according to a metric. 
     
     
         8 . The method of  claim 7 , wherein metric includes at least one of customer satisfaction, conversions, revenue, and profit. 
     
     
         9 . The method of  claim 7 , further comprising understanding and influencing the user based on answers to personality questions or psychological questions. 
     
     
         10 . The method of  claim 1 , further comprising incorporating one or more questions, answers, responses and/or recommendations into the interaction graph/tree that are designed to entertain or increase a user's engagement or satisfaction. 
     
     
         11 . The method of  claim 1 , further comprising optimizing the questions, answers, responses and/or recommendations to learn features about a user and tagging the user with features to help make recommendations for other users with similar features. 
     
     
         12 . The method of  claim 1 , wherein an algorithm for optimizing the interaction tree/graph according to a metric can use at least a portion of information about the user, wherein the information includes at least one of user's answers in a current and/or a prior interaction, user behavior, and/or user demographics. 
     
     
         13 . The method of  claim 1 , wherein an algorithm for optimizing the interaction tree/graph according to a metric can use one or more of an amount of time that elapsed before the user made a decision; a trajectory of a finger, cursor or mouse of the user before the user made the decision; demographic information about the user; personal or personality information about the user; crowd sourced data; information regarding similarity between the user and other users including user features, purchasing preferences, behaviors and tendencies of the other users; and information about a product or products being sold. 
     
     
         14 . The method of  claim 1 , further comprising using reinforcement learning on data available to the computer system to optimize a metric. 
     
     
         15 . The method of  claim 1 , further comprising reorganizing the questions, the answers, the responses and/or the recommendations during execution of the method by the user. 
     
     
         16 . The method of  claim 1 , further comprising reorganizing the questions, the answers, the responses and/or the recommendations based on updated data received by the computer system. 
     
     
         17 . The method of  claim 16 , wherein the updated data includes at least one of personal information about a user, information known about the product or found online, price, shipping speeds, latest reviews, updated manufacturer specifications, and experience of other users that have used the product finder and guide for the particular set of products. 
     
     
         18 . A method for selling a recommended product to a user by providing a computerized product recommendation to the user via a computerized product recommendation engine, the method comprising:
 automatically generating, by the computerized product recommendation engine, an interaction tree/graph comprising questions, answers and responses for a particular set of products;   automatically generating the questions, answers and responses by reviewing data regarding the particular set of products;   dynamically organizing the questions, the answers and the responses as the user answers the questions generated by the computerized product recommendation engine; recommending a product to the user; and   directing a user to a seller of the recommended product.   
     
     
         19 . The method of  claim 18 , further comprising saving personality and psychological information about the user, wherein at least one of the saved personality and psychological information, along with product data, data from other users and optimization data, is retrieved to dynamically organize the questions, the answers and the responses when the user uses the computerized product recommendation engine the first and/or subsequent times. 
     
     
         20 . The method of  claim 18 , further comprising reorganizing the questions, the answers and the responses based on updated data received by the computerized product recommendation engine.